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Record W4388298211 · doi:10.20517/cs.2023.34

Strain engineering of two-dimensional materials for energy storage and conversion applications

2023· article· en· W4388298211 on OpenAlexaff
Xing Peng, Long Chen, Yifan Liu, Chao Liu, Honglan Huang, Jinbo Fan, Pan Xiong, Junwu Zhu

Bibliographic record

VenueChemical Synthesis · 2023
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsStrain engineeringEnergy storageMaterials scienceStrain (injury)Energy transformationMolecular engineeringStrain energyNanotechnologyMechanical engineeringEngineeringStructural engineeringOptoelectronicsFinite element methodPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Two-dimensional (2D) materials have garnered much interest due to their exceptional optical, electrical, and mechanical properties. Strain engineering, as a crucial approach to modulate the physicochemical characteristics of 2D materials, has been widely used in various fields, especially for energy storage and conversion. Herein, the recent progress in strain engineering of 2D materials is summarized for energy storage and conversion applications. The fundamental understanding of strain in 2D materials is first described. Then, some synthetic methods for modulating the properties of 2D materials via strain engineering are introduced. Further, the applications of strain engineering of 2D materials in energy storage, photocatalysis, and electrocatalysis are discussed. Finally, the challenges and perspectives on strain engineering of 2D materials are also outlined.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.230
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2023
Admission routes1
Has abstractyes

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